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DOE OSTI2024

On the Sensitivity of Coastal Hypoxia to Its External Physical Forcings

Abstract The development of low‐oxygen zones threatening marine life (hypoxia) occurs annually in multiple coastal regions of the world. The largest estuary of the continental United States, the Chesapeake Bay, typically has ≈10 km 3 of water with dioxygen concentrations <3 mg L −1 in July. As numerical methods for refining model resolutions in targeted areas are becoming common, there is interest in assessing the feasibility of simulating coastal hazards such as hypoxia in Earth System Models (ESMs). These coupled models are typically not constrained by observations and thus likely to feature systematic biases in their land, atmosphere, or ocean components. This study relies on four numerical experiments to evaluate the sensitivity of Chesapeake Bay hypoxia to changes or biases in its external physical forcings. Hypoxia exhibits only a minor decrease (−1.6%) after reducing the Bay's terrestrial freshwater discharge by 9.5% (but keeping terrestrial nutrient loadings the same). Changes in freshwater discharges have their largest impact on hypoxia during one extreme event (−37% during 2011 tropical storm Lee). Similarly, changing oceanic conditions on the shelf or their temporal frequency impact hypoxia by only 5%–6%, indicating that the latter is predominantly dictated by local conditions. Although these results are promising from the perspective of ESMs, additional components of ESMs will need to be evaluated before general conclusions can be reached. We notably speculate that the Bay's hypoxia would exhibit higher sensitivity to other forcings not examined here, notably air temperatures and nutrient loadings.

Meteorology & Atmospheric Sciences↗

DOE OSTI2020

Rosen Scholar

Dr. Tim Chupp is a professor of physics, applied physics, and biomedical engineering from the University of Michigan. As Rosen Scholar he will work with the Subatomic Physics group’s Neutron Team to develop the LANL RoomTemperature Neutron Electric Dipole Moment (nEDM) experiment that will used at LANSCE's world-leading Ultracold Neutron Facility. The electric dipole moment is the electrical stretching of neutrons or atoms along their spin axis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Advanced Fuels Campaign (2021 Accomplishments)

The 2021 Advanced Fuels Campaign (AFC) Accomplishments Report captures accomplishments made by this long standing, nationally relevant research and development program and reflects the quality and talent of the remarkable people that staff it and the world class scientific facilities they wield. The report documents impactful research that took place in the Campaign in FY21.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Detection of Damage Inducing Impact Events in Vibration Test Fixtures Using Holder Exponents

The Shock and Vibration team within the Test Engineering group at Los Alamos National Laboratory (LANL) aids in the development and qualification of weapon systems for the United States Department of Energy (DOE). Performing tests that replicate shock and vibration environments representative of a weapon’s lifetime is critical in ensuring safety, survivability, and reliability. A key factor in shock and vibration testing is accurate representation of the weapon’s boundary conditions in its end use configuration. The boundary conditions representative of a reentry body in flight are particularly difficult to replicate on an electrodynamic shaker. When executing shock and vibration tests on a reentry body, two system axes are tested with the system in a cantilevered configuration. This cantilevered testing configuration results in a moment load being applied to the system while undergoing vibration environments. A special fixture, known as the Flex-Web, was developed to decouple this moment load from the reentry body in order to more accurately represent real world boundary conditions while replicating flight environments.

42 ENGINEERING↗

DOE OSTI2022

Advanced Distributed Wind Turbine Controls Series: Part 2-Wind Energy in Isolated Grids – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

In an isolated grid, wind turbines are typically deployed to provide energy to maximize energy production, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) this paper demonstrates, through desktop simulations, advanced wind turbine controls that can be employed to support higher contributions of wind in isolated grids, and to demonstrate ways that wind can play a role in supporting stability of an isolated grid. This isolated grid used in these desktop simulations is comprised of a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a simulated diesel generator (2 MW) and various types of loads (critical, dynamic). We developed a model of the subsystems in MATLAB/Simulink and validated them with available data from real-world components on NREL's Flatirons Campus. These validated models are then configured for various case studies. We compare the output of the desktop simulation with a baseline case with the diesel generator. Active and reactive power control of the wind turbine can help improve frequency and voltage responses in the isolated grid, respectively. By utilizing a small integrated battery energy storage system in the DC-link of the wind turbine, we also demonstrate that wind turbines can help blackstart a critical load comparable to its rated power and support other renewables (e.g. solar PV) come online and pick up an additional load. This report illustrates some of these reliability and resilience services a wind turbine can provide in an isolated grid.

17 WIND ENERGY↗

DOE OSTI2021

Science & Technology Review: The Road to Exascale Computing

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The Department of Energy’s Exascale Computing Project (ECP) and Lawrence Livermore’s RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) initiative benefit from strategically developed software tools. The front cover shows a simulation of advection under twisting rotation that uses high-order finite elements from Livermore’s Modular Finite Element Methods (MFEM) software library and GLVis visualization tool. On the back cover, the logo (also created with GLVis) for the MFEM project illustrates the curved mesh and sub-element resolution used in high-order simulations. MFEM and GLVis are key components of the ECP’s co-design Center for Efficient Exascale Discretizations (CEED) and RADIUSS. MFEM is also part of ECP’s Extreme-Scale Scientific Software Development Kit (xSDK).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Seven governing principles in biology

In physical science such as physics and chemistry, there are governing principles that are universal and applicable to all relevant systems, including energy conservation, entropy increase, uncertainty principle in quantum mechanics, and chemical equilibrium. However, what are governing principles in biology that are unique to all living systems? After collecting opinions and thoughts from diverse scientists and engineers all over the world, I summarize seven governing principles or laws in biology: central dogma, evolution, biological robustness, regeneration, reproduction, development, and causality. Some of these are not necessarily unique in biological systems from a reductionist’s point of view (e.g., causality), and others are applicable predominantly to eukaryotes (e.g., reproduction and development). Notably, many engineering systems have mimicked biological systems to enhance their performance. In this perspective article, I discuss these principles to better understand the rules of life and help construct improved engineering systems that we can use and control in an ethical, safe, and rational way.

Moon, Tae Seok↗

DOE OSTI2026

GenASiS: General Astrophysical Simulation System. II. Self-gravitating Baryonic Matter*

GenASiS (General Astrophysical Simulation System) is a code being developed initially and primarily, though not exclusively, for the simulation of core-collapse supernovae on the world's leading capability supercomputers. This paper---the second in a series---documents capabilities for Newtonian self-gravitating fluid dynamics, including tabulated microphysical equations of state treating nuclei and nuclear matter (`baryonic matter'). Computation of the gravitational potential of a spheroid, and simulation of the gravitational collapse of dust and of an ideal fluid, provide tests of self-gravitation against known solutions. In multidimensional computations of the adiabatic collapse, bounce, and explosion of spherically symmetric pre-supernova progenitors---which we propose become a standard benchmark for code comparisons---we find that the explosions are prompt and remain spherically symmetric (as expected), with an average shock expansion speed and total kinetic energy that are inversely correlated with the progenitor mass at the onset of collapse and the compactness parameter.

Cardall, Christian [ORNL] (ORCID:000000020086105X)↗

DOE OSTI2020

U.S. Department of Energy Solar Decathlon Competition Guide 2019-2020

This Solar Decathlon will offer collegiate teams a unique experience to develop critical career skills, learn from both national experts and peers, and gain valuable insights from world-class thought leaders. This document details the Rules for the Design Challenge and Build Challenge.

14 SOLAR ENERGY↗

DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Weakly Supervised Event Classification Using Imperfect Real-world PMU Data with Scarce Labels

This paper studies event classification using imperfect real-world phasor measurement unit (PMU) data with scarce event types (labels). By investigating the real-world PMU data, it is observed that most real-world PMU data's event type is unknown, which makes it challenging to directly use such dataset to build event classifiers as existing classification techniques require high-quality training data with known event type (i.e., label). To address this challenge, a weakly supervised learning based event classification approach is developed, which can use noisy and low-quality PMU data for the training. First, data quality issues are fixed using data preprocessing techniques and then event features are constructed from the PMU data. Using these features, a series of labeling functions are learnt to generate initial estimates of the labels of large amounts of unlabeled PMU data. As the labeling functions are learnt using the same data with scarce labels, the label estimates from the labeling functions can be correlated, noisy, and bias. To enhance these initial estimates, a generative model is developed to characterize the dependencies among the estimated labels, based on which better labels are obtained for training event classifiers. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S. power transmission grid show that the proposed weakly supervised event classifier trained using the dataset with only 5% labeled data can achieve 78.4% classification accuracy.

Liu, Yunchuan↗

DOE OSTI2022

Bioplastic design using multitask deep neural networks

Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Microalgae as a future food source

One of the key challenges that we face in the 21st century is the need to feed an ever-increasing human population with increasingly limited natural resources. Even today it is estimated that roughly 1 out of 9 people in the world are undernourished, of which the most important factor is protein-energy malnutrition. By establishing microalgae as a new food and feed platform, we have the opportunity to increase the supply of these essential products to address global demands in a more efficient and environmentally sustainable way. Many types of algae are nutritionally complete foods, their yields outperform most plant crops, and there is a growing set of tools to develop improved strains of algae. Similar improvements were achieved in traditional crops through thousands of years of breeding and strain selection, whereas with the newest genetic engineering tools and advanced strain selection techniques, similar changes can be implemented in microalgae in just a few years. Here we describe different strategies that could be used to enhance the nutritional content, productivity, and organoleptic traits of algae to help drive development of this new crop. Clearly developing more efficient, sustainable, and nutritious foods and feed would be an enormous benefit for the planet, and algae represents an opportunity to develop a new crop that would complement traditional agriculture, and one that could potential result in a more efficient means to meet the world's food and feed supply.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

AMPPED LDRD: Advanced Models of the Physics & Phenomena of Electrical Discharge.

This Laboratory Directed Research and Development (LDRD) project aimed at addressing an unmet need for physics-based models that enable prediction of electrical breakdown in the presence of high voltage gradients and real-world environments. This project focused on understanding coupled plasma-surface feedback phenomena critical to electrical discharge processes and design of breakdown voltages, breakdown times, and breakdown paths. New secondary electron emission and coupled plasma-surface measurement methods and models were developed. Highly reproducible discharge experiments were developed to provide current-voltage data as a function of time, and methods were developed to condition electrodes for predictable discharge. To assess plasma-surface feedback mechanisms, new surface physics capabilities, including measurement systems for local and global photoemission yield and ion-induced secondary electron emission yield were developed. Discharge models were developed to incorporate these yields for predicting discharge behavior to understand influence of surface physics on discharge current and time evolution, and methods to accelerate these simulations were demonstrated. A key finding was that the initiation process of a non-vacuum discharge appears highly dependent on photoemission at the cathode. Advanced diagnostics were developed, including spatial electrical field imaging, spectroscopy of plasmas, and high-speed imaging of discharge event s in metal and dielectric environments. These diagnostics and improved electrical models are a step toward enabling quantitative design margins, assurance of safety architectures, and implementation of novel materials and technologies for wide ranging energy and electronics applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Design and Development of the Handling Frame Assembly Table for the NIF Sustainment Project (Draft)

The Handling Frame Assembly Table is a custom designed piece of equipment developed to support the NIF Sustainment Project. The NIF Sustainment project's goal is to refurbish the National Ignition Facility (NIF), the world’s largest and most energetic laser, so it can continue advancing research in fusion energy, national security, and high energy density physics. A key aspect of this effort involves replacing all 1,728 blast shields on the NIF, which requires specialized equipment such as the Handling Frame Assembly Table to support the production of new blast shields. This project follows a systems engineering approach that guides the design sequence. This report documents the design process from initial concept through final design, including stakeholder analysis, requirements development, conceptual design, design analysis and validation, and final design. Future work will focus on building and commissioning the system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A simulation pipeline for fast neutron imaging and spectroscopy using quantified detector attributes

Radiation imaging capabilities, essential in the nuclear nonproliferation regime, facilitate source localization and, in certain cases, spectroscopy. Scatter-based neutron cameras, which can measure the neutron signatures from special nuclear material, hold particular interest. Systems incorporating organic scintillators can extract neutron energy spectra, potentially distinguishing fission neutron sources from others, such as alpha-neutron sources. The development and testing of a scatter-based neutron imager, however, can be challenging without having an accurate simulation model or first constructing a prototype. This work describes a simulation pipeline that takes output from MCNPX-PoliMi simulations and creates the expected back-projection neutron images and neutron energy spectra. This pipeline was developed to improve the modeling of fast neutron imagers and bridge the current gap in literature, which predominantly focuses on gamma-ray Compton imager models. This work also reports on the significance of various real-world system considerations and their effects on the simulated detector responses. The pipeline was verified and validated with experimental data collected using a 252 Cf spontaneous fission source using a fast neutron scattering imager developed at the University of Michigan.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗